Learned graph embeddings can be used for a wide variety of tasks, including node classification, relation prediction, and clustering [1]}.
Here, we focus on the relation prediction task, i.e., using the learned representations to predict previously unobserved relationships between the input nodes.
The relation prediction task is exceptionally general—for example, it generalizes basic recommender systems, knowledge base completion, and even node classification (ibid.).
